Artificial intelligence model for prediction of cardiogenic shock in patients with acute coronary syndrome. (2nd May 2022) - Bohm, A; Jajcay, N; Jankova, J; Petrikova, K; Bezak, B
Abstract: Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministry of Education, Science, Research and Sport of the Slovak Republic VEGA Background: Cardiogenic shock (CS) is a serious life-threatening condition affecting almost 10% of patients suffering from acute coronary syndrome (ACS). Despite recent treatment improvements such as mechanical circulatory support devices (MCS), mortality remains 50%. It is hypothesized that early implantation of MCS before hemodynamic deterioration could prevent CS. Therefore, knowing which ACS patient will progress into CS would be of paramount importance. Purpose: We aimed to develop a model based on machine learning algorithms for CS prediction in patients with ACS. Method: Over 40 000 patients from critical care units of the Beth Israel Deaconess Medical Center database were extensively analyzed and sorted. Patients suffering from acute coronary syndrome undergoing cardiac catheterizations were selected and divided into two groups based on the development of CS. Patients in CS at the time of admission were excluded. The study population consisted of 3056 patients who didn't develop CS and of 176 patients who did develop CS. Important information was also extracted manually from textual summaries of hospital stays. Potentially relevant clinical variables for shock prediction were selected using supervised feature selection, and missing values were supplemented using imputation methods. Seven well-known and established machine learning algorithms were used. Based on preliminary evaluation of classifier performance (AUC on random train-test split with 30% test data), we selected two best-performing algorithms: Logistic Regression and Gaussian Process classifier with Radial Basis Function (RBF) kernel. Both models were subsequently validated using Repeated Stratified K-Fold cross-validation with 5 folds and 20 repeats each. Results: Age, heart rate, mean arterial pressure, respiratory rate, oxygen flow (liters per minute), peripheral oxygen saturation, blood glucose, pain type, heart rhythm and ectopy frequency were chosen as input variables. Both models showed good discrimination. The Logistic Regression model scored AUC of 0.76 ± 0.04. The Gaussian Process classifier with Radial Basis Function (RBF) kernel scored AUC of 0.77 ± 0.03. Conclusion: According to our knowledge this is the first study that uses machine learning algorithms to predict CS in patients with ACS based on easily obtainable clinical variables. Further explorations and refinement of used imputation, feature selection, machine learning techniques and validation on an external cohort might result in even better performance of the proposed prediction models. Furthermore, these prediction models could be transformed into a simple predictive scoring system available in clinical practice.